Some recent works also use the label semantics information, such as [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}. The similarity between our method with them is that: they all use the label semantic information. But on (1) the way of application, and (2) the way to obtain label semantics are very different from each other.
(1) On the way of application:
These methods all need to design an extra word2vector network to convert the label semantic information into label embedding, and then splices it with visual embedding.
While our CSN directly uses the label semantics, no need for additional networks, only need to design a multi-class loss in the pre-train network.
(2) On the way to obtain label semantics:
In the comparison methods, each sample has a variety of labels. These labels are manually annotated, which are very accurate but difficult to obtain. Therefore, these methods lack universality.
While in our CSN, we use the existing toolkit WordNet [8]} to generate new labels for samples of the same category. The accuracy of the generated multi-labels is definitely far from the manually annotated labels, but it greatly reduces the labor cost and makes our method more practical.
